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Overview

This guide was created for versions: v0.0.0 - v0.9.1

Welcome,to the ComputeCpp CE Developer Portal & SDK.

Welcome to the ComputeCPP CE SDK and reference guide. If you are new to ComputeCpp, please download the latest version and read through the ‘Getting Started Guide’. If you're an experienced developer, you may want to skip ahead to the API Reference Guide.

Accelerate your application with ComputeCpp

ComputeCpp also works with a number of frameworks including ParallelSTl and VisionCpp. ComputeCpp, Codeplay's implementation of the open standard SYCL, enables you to integrate parallel computing into your application and accelerate your code across OpenCL devices such as GPUs. Applications that require a large number of common operations can make huge performance improvements by running the operations in parallel on OpenCL devices. For example, the neural networks used in machine learning perform huge numbers of matrix calculations and ComputeCpp can be used to run these operations in parallel, vastly increasing performance and reducing the power used by the application.

With ComputeCpp and SYCL you can write code once and execute on a range of OpenCL enabled devices reducing your development effort. Develop with standard C++ and the SYCL open standard, re-using your existing C++ libraries. ComputeCpp is also building support for C++17 Parallel STL enabling parallelized library functions to run on accelerated processors. ComputeCpp also works with a number of frameworks including ParallelSTL and VisionCpp.

Who is ComputeCpp for?

Portable Parallel Computing Applications

OpenCL devices such as GPUs can be used to accelerate applications by running operations in parallel. By implementing ComputeCpp using the SYCL open standard, developers can write software with C++ single source and run their code using parallel computing across a range of OpenCL devices.

Using TensorFlow with ComputeCpp

Machine learning framework TensorFlow requires large amounts of vector and matrix operations. Performance and power consumption can be vastly improved by using parallel computing. ComputeCpp enables developers to target OpenCL devices such as GPUs using modern C++ code.

Complex Mathematical Applications

The Eigen library is one of the most popular C++ libraries for linear algebra, matrix and vector operations and related algorithms. Eigen is integrated with ComputeCpp enabling developers to run these operations on OpenCL devices. By taking advantage of these parallel architectures, applications can be accelerated.